Video-based AI for beat-to-beat assessment of cardiac function

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

Heart failure (HF) is a major public health issue, with a prevalence of over 5.8 million in the USA, and over 23 million worldwide, and rising. The lifetime risk of developing HF is one in five. Although promising evidence shows that the age-adjusted incidence of HF may have plateaued, HF still carries substantial morbidity and mortality, with 5-year mortality that rival those of many cancers. HF represents a considerable burden to the health-care system, responsible for costs of more than $39 billion annually in the USA alone, and high rates of hospitalizations, readmissions, and outpatient visits. HF is not a single entity, but a clinical syndrome that may have different characteristics depending on age, sex, race or ethnicity, left ventricular ejection fraction (LVEF) status, and HF etiology. Furthermore, pathophysiological differences are observed among patients diagnosed with HF and reduced LVEF compared with HF and preserved LVEF, which are beginning to be better appreciated in epidemiological studies. A number of risk factors, such as ischemic heart disease, hypertension, smoking, obesity, and diabetes, among others, have been identified that both predict the incidence of HF as well as its severity. In this Review, we discuss key features of the epidemiology and risk profile of HF.

The objectives of the present study were to describe epidemiology and outcomes in ambulatory heart failure (HF) patients stratified by left ventricular ejection fraction (LVEF) and to identify predictors for mortality at 1 year in each group.